DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics.
Nat Commun
; 13(1): 1347, 2022 03 15.
Article
em En
| MEDLINE
| ID: mdl-35292629
ABSTRACT
The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.
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1
Base de dados:
MEDLINE
Assunto principal:
Metabolômica
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article